论文标题
部分可观测时空混沌系统的无模型预测
Distributed Nonparametric Estimation under Communication Constraints
论文作者
论文摘要
在大数据的时代，有必要使用分布式数据跨多个计算节点和构造估计器划分极大的数据集。在设计分布式估计器时，希望最大程度地减少整个网络的通信量，因为与一台计算机中的计算相比，计算机之间的传输速度很慢。我们的工作提供了一个一般框架，以理解在通信约束中针对非参数问题的分布式估计的行为。我们为广泛的模型提供了结果，超越了主导文献的高斯框架。作为具体的示例，我们在分布式回归，密度估计，分类，泊松回归和波动率估计模型中得出最小值和匹配的上限。为了协助这一点，我们提供了足够的条件，可以在所有示例中易于验证。
In the era of big data, it is necessary to split extremely large data sets across multiple computing nodes and construct estimators using the distributed data. When designing distributed estimators, it is desirable to minimize the amount of communication across the network because transmission between computers is slow in comparison to computations in a single computer. Our work provides a general framework for understanding the behavior of distributed estimation under communication constraints for nonparametric problems. We provide results for a broad class of models, moving beyond the Gaussian framework that dominates the literature. As concrete examples we derive minimax lower and matching upper bounds in the distributed regression, density estimation, classification, Poisson regression and volatility estimation models under communication constraints. To assist with this, we provide sufficient conditions that can be easily verified in all of our examples.
